Electronic digital convolutions could extract key features of objects for data processing and information identification in artificial intelligence, but they are time-cost and energy consumption due to the low response of electrons. Although massless photons enable high-speed and low-loss analog convolutions, two existing all-optical approaches including Fourier filtering and Green’s function have either limited functionality or bulky volume, thus restricting their applications in smart systems. Here, we report all-optical convolutional computing with a metasurface-singlet or -doublet imager, considered as the third approach, where its point spread function is modified arbitrarily via a complex-amplitude meta-modulator that enables functionality-unlimited kernels. Beyond one- and two-dimensional spatial differentiation, we demonstrate real-time, parallel, and analog convolutional processing of optical and biological specimens with challenging pepper-salt denoising and edge enhancement, which significantly enrich the toolkit of all-optical computing. Such meta-imager approach bridges multi-functionality and high-integration in all-optical convolutions, meanwhile possessing good architecture compatibility with digital convolutional neural networks.

A new 3DVAR-based Ocean Variational Analysis System (OVALS) is developed. OVALS is capable of assimilating in situ sea water temperature and salinity observations and satellite altimetry data. As a component of OVALS, a new variational scheme is proposed to assimilate the sea surface height data. This scheme considers both the vertical correlation of background errors and the nonlinear temperature-salinity relationship which is derived from the generalization of the linear balance constraints to the nonlinear in the 3DVAR. By this scheme, the model temperature and salinity fields are directly adjusted from the altimetry data. Additionally, OVALS can assimilate the temperature and salinity profiles from the ARGO floats which have been implemented in recent years and some temperature and salinity data such as from expendable bathythermograph, moored ocean buoys, etc. A 21-year assimilation experiment is carried out by using OVALS and the Tropical Pacific circulation model. The results show that the assimilation system may effectively improve the estimations of temperature and salinity by assimilating all kinds of observations. Moreover, the root mean square errors of temperature and salinity in the upper depth less than 420 m reach 0.63℃ and 0.34 psu. Keywords: data assimilation, 3DVAR, sea surface height, ARGO floats.With the successful development of ocean buoys technique in the 1990s, the ARGO plan was proposed by the international group in 1998 and implemented in 1999. Up to now, more than 1800 ARGO floats are now in operation and delivering real-time temperature and salinity profile observations over the world oceans. By the end of 2005, ARGO will expect to provide more than 100000 temperature and salinity profiles which have a depth range of 0 m to 2000 m [1] . Apart from the ARGO plan, the GODAE high-resolution sea surface temperature pilot project (GHRSST-PP) is beginning to implement to provide high resolution in the time of 6 h and in the space of 10 km [2] sea surface temperature observation with global coverage for the operational oceanography, climate study and prediction. Except the above two new observation systems, as a successor of successful implementation of Topex/Poseidon altimeter satellite in the 1990s, the Jason-1 Mission was successfully launched in December, 2001. This observation system can provide

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